Generating Natural Motion in an Android
by Mapping Human Motion
Daisuke Matsui∗ , Takashi Minato∗ , Karl F. MacDorman† , and Hiroshi Ishiguro∗‡∗ Department of Adaptive Machine Systems, Graduate School of Engineering, Osaka University

2-1 Yamada-oka, Suita, Osaka 565-0871 Japan
Email: matsui@ed.ams.eng.osaka-u.ac.jp† Frontier Research Center, Osaka University
2-1 Yamada-oka, Suita, Osaka 565-0871 Japan‡ Intelligent Robotics and Communication Laboratories, Advanced Telecommunications Research Institute International
2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288 Japan

Abstract— One of the main aims of humanoid robotics is todevelop robots that are capable of interacting naturally withpeople. However, to understand the essence of human interaction, it is crucial to investigate the contribution of behavior andappearance. Our group’s research explores these relationships
by developing androids that closely resemble human beings inboth aspects. If humanlike appearance causes us to evaluate anandroid’s behavior from a human standard, we are more likely
to be cognizant of deviations from human norms. Therefore,
the android’s motions must closely match human performance to
avoid looking strange, including such autonomic responses as theshoulder movements involved in breathing. This paper proposesa method to implement motions that look human by mapping
their three-dimensional appearance from a human performer
to the android and then evaluating the verisimilitude of thevisible motions using a motion capture system. This approach
has several advantages over current research, which has focusedon copying a person’s moving joint angles to a robot: (1) in an
android robot with many degrees of freedom and kinematics thatdiffers from that of a human being, it is difﬁcult to calculate
which joint angles would make the robot’s posture appear
similar to the human performer; and (2) the motion that weperceive is at the robot’s surface, not necessarily at its joints,
which are often hidden from view.
Index Terms— Learning control systems, motion analysis,
humanlike motion, human-robot imitation, android science,
appearance and behavior problem.

I. I NTRODUCTION
Much effort in recent years has focused on the developmentof such mechanical-looking humanoid robots as Honda’s
Asimo and Sony’s Qrio with the goal of partnering them withpeople in daily situations. Just as an industrial robot’s purpose
determines its appearance, a partner robot’s purpose will also
determine its appearance. Partner robots generally adopt aroughly humanoid appearance to facilitate communication
with people, because natural interaction is the only task that
requires a humanlike appearance. In other words, humanoid
robots mainly have signiﬁcance insofar as they can interact
naturally with people. Therefore, it is necessary to discover
the principles underlying natural interaction to establish amethodology for designing interactive humanoid robots.
Kanda et al. [1] have tackled this problem by evaluating how the behavior of the humanoid robot “Robovie”

affects human-robot interaction. But Robovie’s machine-likeappearance distorts our interpretation of its behavior becauseof the way the complex relationship between appearance
and behavior inﬂuences the interaction. Most research oninteractive robots has not evaluated the effect of appearance (for exceptions, see [2] [3]) — and especially not in arobot that closely resembles a person . Thus, it is not yet
clear whether the most comfortable and effective humanrobot communication would come from a robot that looks
mechanical or human. However, we may infer a humanlike
appearance is important from the fact that human beings havedeveloped neural centers specialized for the detection andinterpretation of hands and faces [4] [5] [6]. A robot thatclosely resembles humans in both looks and behavior may
prove to be the ultimate communication device insofar as it
can interact with humans the most naturally.1 We refer tosuch a device as an android to distinguish it from mechanicallooking humanoid robots. When we investigate the essence of
how we recognize human beings as human, it will become
clearer how to produce natural interaction. Our study tackles
the appearance and behavior problem with the objective of
realizing an android and having it be accepted as a humanbeing [7].
Ideally, to generate humanlike movement, an android’s
kinematics should be functionally equivalent to the humanmusculoskeletal system. Some researchers have developed
a joint system that simulates shoulder movement [8] and amuscle-tendon system to generate humanlike movement [9].
However, these systems are too bulky to be embedded in
an android without compromising its humanlike appearance.
Given current technology, we embed as many actuators as
possible to provide many degrees of freedom insofar as thisdoes not interfere with making the android look as human as
possible [7]. Under these constraints, the main issue concerns
how to move the android in a natural way so that its movement
may be perceived as human.
A straightforward way to make a robot’s movement more
1 We use the term natural to denote communication that ﬂows without
seeming stilted, forced, bizarre, or inhuman.

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familiarity

humanlike is to imitate human motion. Kashima and Isurugi[10] extracted essential properties of human arm trajectories
and designed an evaluation function to generate robot arm
trajectories accordingly. Another method is to copy humanmotion as measured by a motion capture system to a humanoid robot. Riley et al. [11] and Nakaoka et al. [12]calculated a performer’s joint trajectories from the measured
positions of markers attached to the body and fed them tothe joints of a humanoid robot. In these studies the authors
assumed the kinematics of the robot to be similar to that ofa human body. However, the more complex the robot’s kinematics, the more difﬁcult it is to calculate which joint angles
will make the robot’s posture similar to the performer’s joint
angles as calculated from motion capture data. Therefore,
it is possible the assumption that the two joint systems arecomparable results in visibly different motion in some cases.
This is especially a risk for androids because their humanlike
form makes us more sensitive to deviations from humanways of moving. Thus, slight differences could strongly
inﬂuence whether the android’s movement is perceived as
natural or human. Furthermore, these studies did not evaluate
the naturalness of robot motions.
Hale et al. [13] proposed several evaluation functions
to generate a joint trajectory (e.g., minimization of jerk)
and evaluated the naturalness of generated humanoid robot
movements according to how human subjects rated theirnaturalness. In the computer animation domain, researchers
have tackled a motion synthesis with motion capture data
(e.g., [14]). However, we cannot apply their results directly;
we must instead repeat their experiment with an android
because the results from an android testbed could be quite
different from those of a humanoid testbed. For example,
Mori described a phenomenon he termed the “uncanny valley”[15], [16], which relates to the relationship between how
humanlike a robot appears and a subject’s perception offamiliarity. According to Mori, a robot’s familiarity increases
with its similarity until a certain point is reached at whichslight “nonhuman” imperfections cause the robot to appear
repulsive (Fig. 1). This would be an issue if the similarityof androids fell into the chasm. (Mori believes mechanicallooking humanoid robots lie on the left of the ﬁrst peak.) Thisnonmonotonic relationship can distort the evaluation proposedin existing studies. Therefore, it is necessary to develop a
motion generation method in which the generated “android
motion” is perceived as human.
This paper proposes a method to transfer human motion
measured by a motion capture system to the android by copying changes in the positions of body surfaces. This method
is called for because the android’s appearance demands
movements that look human, but its kinematics is sufﬁciently
different that copying joint-angle information would not yield
good results. Comparing the similarity of the android’s visible
movement to that of a human being enables us to develop
more natural movements for the android.
In the following sections, we describe the developed android and mention the problem of motion transfer and our

uncanny valley

healthyhuman

humanoid robot

toy

100%

similarity
moving corpse

Fig. 1.

Fig. 2.

Uncanny valley

The developed android “Repliee Q2”

basic idea about the way to solve it. Then we describe theproposed method in detail and show experimental results fromapplying it to the android.
II. T HE A NDROID
Fig. 2 shows the developed android called Repliee Q2. The
android resembles an Asian woman because it is modeled
after a Japanese woman. The standing height is about 160
cm. The skin is composed of a kind of silicone that has
a humanlike feel and neutral temperature. The silicone skincovers the upper torso, neck, head, and forearms with clothing
covering other body parts. Unlike Repliee R1 [17], [7],silicone skin does not cover the entire body so as to facilitate
ﬂexibility and a maximal range of motion. The soft skingives the android a human look and enables natural tactileinteraction. To lend realism to the android’s appearance, wetook a cast of a person to mold the android’s skin. Forty-two
highly sensitive tactile sensors composed of piezo diaphragms

Fig. 3.

Examples of motion and facial expressions

Feedforward
control inputfFeedforward
controller

Humanmarker pos.
hx

Estimated
Error ofhuman
joint angle joint angle
Transform

T: xh q^ h

q^ h + Dq^ dq^ a
Estimated
androids
joint angle

Transform+

Androids
joint angleaq

Dqb + qd

Feedback
control input

Android

ControlinputxaAndroidsmarker pos.

T: xa q^ a

The android control system

TABLE IT HE D O F CONFIGURATION OF R EPLIEE Q2
Eyes
Face
Mouth
Neck
Shoulder
Elbow
Wrist
Fingers
Torso

aDqb

Feedback controller-

Fig. 4.

Error
signalq

Degree of freedom
pan×2 + tilt×1
eyebrows×1 + eyelids×1 + cheeks×1
7 (including the upper and lower lips)
3
5×2
2×2
2×2
2×2
4

are mounted under the android’s skin and clothes throughout
the body, except for the shins, calves, and feet. Since theoutput value of each sensor corresponds to its deforming rate,
the sensors can distinguish different kinds of touch ranging
from stroking to hitting.
The android is driven by air actuators that give it 42 degrees
of freedom (DoFs) from the waist up. (The legs and feet are
not powered.) The conﬁguration of the DoFs is shown in
Table I. The android can generate a wide range of motions
and gestures as well as various kinds of micro-motions such as
the shoulder movements typically caused by human breathing.
The DoFs of the shoulders enable them to move up and down
and backwards and forwards. Furthermore, the android canmake some facial expressions and mouth shapes, as shownin Fig. 3. The compliance of the air actuators makes for asafer interaction with movements that are generally smoother.
Because the android has servo controllers, it can be controlled
by sending desired joint positions from a host computer.
Parallel link mechanisms adopted in some parts complicate
the kinematics of the android.
III. T RANSFERRING H UMAN M OTION
A. The basic idea
One method to realize humanlike motion in a humanoid
robot is through imitation. Thus, we consider how to map
human motion to the android. Most previous research assumes
the kinematics of the human body is similar to that of therobot except for the scale. Thus, they aim to reproduce humanmotion by reproducing kinematic relations across time and,in particular, joint angles between links. For example, thethree-dimensional locations of markers attached to the skin
are measured by a motion capture system, the angles of thebody’s joints are calculated from these positions, and theseangles are transferred to the joints of the humanoid robot. It
is assumed that by using a joint angle space (which does not
represent link lengths), morphological differences between thehuman subject and the humanoid robot can be ignored.
However, there is potential for error in calculating a joint
angle from motion capture data. The joint positions are
assumed to be the same between a humanoid robot andthe human performer who serves as a model; however,
the kinematics in fact differs. For example, the kinematics
of Repliee Q2’s shoulder differs signiﬁcantly from thoseof human beings. Moreover, as human joints rotate, eachjoint’s center of rotation changes, but joint-based approaches
generally assume this is not so. These errors are perhaps more
pronounced in Repliee Q2, because the android has manydegrees of freedom and the shoulder has a more complex
kinematics than existing humanoid robots. These errors are
more problematic for an android than a mechanical-looking
humanoid robot because we expect natural human motion
from something that looks human and are disturbed whenthe motion instead looks inhuman.
To create movement that appears human, we focus on
reproducing positional changes at the body’s surface rather
than changes in the joint angles. We then measure the posturesof a person and the android using a motion capture system
and ﬁnd the control input to the android so that the posturesof person and android become similar to each other.
B. The method to transfer human motion
We use a motion capture system to measure the posturesof a human performer and the android. This system canmeasure the three-dimensional positions of markers attachedto the surface of bodies in a global coordinate space. First,
some markers are attached to the android so that all joint
motions can be estimated. The reason for this will become

Error ofmarker pos.

Humanmarker pos.
h

x

+

D xd

-

Androidsmarker pos.

Feedback controller+

Dqb + qd

Androidxa(a) Feedback of maker position error

Humanmarker pos.
hx

Estimated
Error ofhuman
joint angle joint angle
Transform

T: xh q^ h

qh + Dqd^Androids
joint angle^-

Feedback controller

Dqb +

+

qd

Androidqa(b) Error estimation with the androids joint angle measured by the potentiometerHumanmarker pos.
hx

Estimated
Error ofhuman
joint angle joint angle
Transform

T: xh q^ h

qh + Dqd^

Androidsmarker pos.

^-

Feedback controllerq^ aEstimated
androids
joint angle+

Dqb + qd

Androidxa

Transform

T: xa q^ a(c) Error estimation with the androids joint angle estimated from the androids marker position
Fig. 5.

The feedback controller with and without the estimation of the android’s joint angle

clear later. Then the same number of markers are attached
to corresponding positions on the performer’s body. We must
assume the android’s surface morphology is not too different
from the performer’s.
We use a three-layer neural network to construct a mappingfrom the performer’s posture to the android’s control input,
which is the desired joint angle. The reason for the network isthat it is difﬁcult to obtain the mapping analytically. To train aneural network to map from xh to q a would require thousandsof pairs of xh , q a as training data, and the performer would
need to assume the posture of the android for each pair. We
avoid this prohibitively lengthy task in data collection by
adopting feedback error learning (FEL) to train the neuralnetwork. Kawato et al. [18] proposed feedback error learning
as a principle for learning motor control in the brain. This
employs an approximate way of mapping sensory errors to
motor errors that subsequently can be used to train a neuralnetwork (or other method) by supervised learning. Feedbackerror learning neither prescribes the type of neural network
employed in the control system nor the exact layout of thecontrol circuitry. We use it to estimate the error between the
postures of the performer and the android and feed the error
back to the network.
Fig. 4 shows the block diagram of the control system,
where the network mapping is shown as the feedforward
controller. The weights of the feedforward neural network arelearned by means of a feedback controller. The method has

a two-degrees-of-freedom control architecture. The network
tunes the feedforward controller to be the inverse modelof the plant. Thus, the feedback error signal is employed
as a teaching signal for learning the inverse model. If theinverse model is learned exactly, the output of the plant tracks
the reference signal by feedforward control. The performer
and android’s marker positions are represented in their local
coordinates xh , xa ∈ R3m ; the android’s joint angles q a ∈Rn can be observed by a motion capture system and apotentiometer, where m is the number of markers and n is
the number of DoFs of the android.
The feedback controller is required to output the feedback
control input ∆q b so that the error in the marker’s position∆xd = xa − xh converges to zero (Fig. 5(a)). However, it
is difﬁcult to obtain ∆q b from ∆xd . To overcome this, we
assume the performer has roughly the same kinematics as
the android and obtain the estimated joint angle q̂ h simply
by calculating the Euler angles (hereafter the transformationfrom marker positions to joint angles is described as T ).2
Converging q̂ a to q h does not always produce identicalpostures because q̂ h is an approximate joint angle that may
include transformation error (Fig. 5(b)). Then we obtain
2 There are alternatives to using the Euler angles such as angle decomposition [19], which has the advantage of providing a sequence independent
representation, or least squares, to calculate the helical axis and rotational
angle [20] [21]. This last method provides higher accuracy when manymarkers are used but has an increased risk of marker crossover.

Position vectors on theshoulder and chest (5)MarkerMarkerPosition
vectorp1,...,p3

Coordinates fixed to the chest
Position vectors
on the head (3)Coordinates
fixed to the chest

MarkerPosition
vectorp4,...,p8
Coordinates
fixed to the waist

Position
vectorp9,...,p20
a vector connecting
two neighboring markers

MarkerPosition vectors on
the arms (12)Performer
Fig. 6.

x=

p1
.
.
.
p20

[]

Android

The marker positions corresponding to each otherthe estimated joint angle of the android q̂ a using the sametransformation T and the feedback control input to convergeq̂ a to q̂ h (Fig. 5(c)). This technique enables xa to approach
xh . The feedback control input approaches zero as learning
progresses, while the neural network constructs the mappingfrom xh to the control input q d . We can evaluate the apparent
posture by measuring the android posture.
In this system we could have made another neural network
for the mapping from xa to q a using only the android. Aslong as the android’s body surfaces are reasonably close
to the performer’s, we can use the mapping to make thecontrol input from xh . Ideally, the mapping must learn every
possible posture, but this is quite difﬁcult. Therefore, it is stillnecessary for the system to evaluate the error in the apparent
posture.
IV. E XPERIMENT TO T RANSFER H UMAN M OTION
A. Experimental setting
To verify the proposed method, we conducted an experiment to transfer human motion to the android Repliee Q2.
We used 21 of the android’s 42 DoFs by excluding the 13
DoFs of the face, the 4 of the wrists, and the 4 of the
ﬁngers (n = 21). We used a Hawk Digital System,3 which
can track more than 50 markers in real-time. The system ishighly accurate with a measurement error of less than 1 mm.
Twenty markers were attached to the performer and another
20 to the android as shown in Fig. 6 (m = 20). Because
the android’s waist is ﬁxed, the markers on the waist set
the frame of reference for an android-centered coordinate
space. To facilitate learning, we introduce a representationof the marker position xh , xa as shown in Fig. 7. The effect
3 Motion Analysis Corporation, Santa Rosa, California.
http://www.motionanalysis.com/

Fig. 7. The representation of the marker positions. A marker’s diameter isabout 18 mm.

of waist motions are removed with respect to the markers
on the head. To avoid accumulating the position errors at
the end of the arms, vectors connecting neighboring pairs ofmarkers represent the positions of the markers on the arms.
We used arc tangents for the transformation T , in which thejoint angle is an angle between two neighboring links wherea link consists of a straight line between two markers.
The feedback controller outputs ∆q b = K∆q̂ d , where thegain K consists of a diagonal matrix. There are 60 nodes
in the input layer (20 markers × x, y, z), 300 in the hidden
layer, and 21 in the output layer (for the 21 DoFs). Using 300
units in the hidden layer provided a good balance between
computational efﬁciency and accuracy. Using signiﬁcantly
fewer units resulted in too much error, while using signiﬁcantly more units provided only marginally higher accuracy
but at the cost of slower convergence. The error signal to the
network is t = α∆q b , where the gain α is a small number.
The sampling time for capturing the marker positions andcontrolling the android is 60 ms. Another neural network
which has the same structure previously learned the mappingfrom xa to q a to set the initial values of the weights. We
obtained 50,000 samples of training data (xa and q a ) bymoving the android randomly. The learned network is used
to set the initial weights of the feedforward network.
B. Experimental results and analysis
1) Surface similarity between the android and performer:
The proposed method assumes a surface similarity between
the android and the performer. However, the male performer
whom the android imitates in the experiments was 15 cm
taller than the women after whom the android was modeled.
To check the similarity, we measured the average distance
between corresponding pairs of markers when the android

70
500

60

Height of the fingers [mm]Average of feedback error

65

55
50
45
40
35

400
Performer
Android
Error

300

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100

0

30
25

0

20

0

100

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300

400

500

600

5

700

10

15

20

25

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Time [x60 msec]

Step

Fig. 10.

Fig. 8. The change of the feedback control input with learning of the
network

Average error of marker position [mm]

140

120

100

80

60

40

20

0

20

40

60

80

100

120

140

160

Step

Fig. 9.

The change of the position error with learning of the networkand performer make each of the given postures; the value
was 31 mm (see the Fig. 6). The gap is small compared tothe size of their bodies, but it is not small enough.
2) The learning of the feedforward network: To show theeffect of the feedforward controller, we plot the feedback
control input averaged among the joints while learning fromthe initial weights in Fig. 8. The abscissa denotes the time
step (the sampling time is 60 ms.) Although the value ofthe ordinate does not have a direct physical interpretation,
it corresponds to a particular joint angle. The performer
exhibited various ﬁxed postures. When the performer started
to make the posture at step 0, error increased rapidly because
network learning had not yet converged. The control input
decreases as learning progresses. This shows that the feedforward controller learned so that the feedback control input
converges to zero.
Fig. 9 shows the average position error of a pair ofcorresponding markers. The performer also gave an arbitraryﬁxed posture. The position errors and the feedback control
input both decreased as the learning of the feedforward
network converged. The result shows the feedforward network
learned the mapping from the performer’s posture to theandroid control input, which allows the android to adopt
the same posture. The android’s posture could not match

The step response of the android

the performer’s posture when the weights of the feedforward
network were left at their initial values. This is because theinitial network was not given every possible posture in the
pre-learning phase. The result shows the effectiveness of themethod to evaluate the apparent posture.
3) Performance of the system at following fast movements:
To investigate the performance of the system, we obtained
a step response using the feedforward network after it hadlearned enough. The performer put his right hand on his kneeand quickly raised the hand right above his head. Fig. 10shows the height of the ﬁngers of the performer and android.
The performer started to move at step 5 and reached the ﬁnal
position at step 9, approximately 0.24 seconds later. In thiscase the delay is 26 steps or 1.56 seconds. The arm moved
at roughly the maximum speed permitted by the hardware.
The android arm cannot quite reach the performer’s position
because the performer’s position was outside of the android’s
range of motion. Clearly, the speed of the performer’s movement exceeds the android’s capabilities. This experiment is
an extreme case. For less extreme gestures, the delay will bemuch less. For example, for the sequence in Fig. 11, the delay
was on average seven steps or 0.42 seconds.
4) The generated android motion: Fig. 11 shows the performer’s postures during a movement and the corresponding
postures of the android. The value denotes the time step.
The android followed the performer’s movement with somedelay (the maximum is 15 steps, that is, 0.9 seconds). The
trajectories of the positions of the android’s markers are
considered to be similar to those of the performer, but errors
still remain, and they cannot be ignored. While we canrecognize that the android is making the same gesture as theperformer, the quality of the movement is not the same. There
are a couple of major causes of this:•The kinematics of the android is too complicated torepresent with an ordinary neural network. To avoid thislimitation, it is possible to introduce the constraint of thebody’s branching in the network connections. Anotheridea is to introduce a hierarchical representation of the
mapping. A human motion can be decomposed into adominant motion that is at least partly driven consciously

0

35

80

2

35

90

140

215

260

154

215

270

305

380

410

320

387

415

Fig. 11.
The generated android’s motion compared to the performer’s
motion. The number represents the step.

and secondary motions that are mainly nonconscious(e.g., contingent movements to maintain balance, such
autonomic responses as breathing). We are trying toconstruct a hierarchical representation of motion not only
to reduce the computational complexity of learning but
to make the movement appear more natural.
• The method deals with a motion as a sequence of
postures; it does not precisely reproduce higher orderproperties of motion such as velocity and acceleration
because varying delays can occur between the performer’s movement and the android’s imitation of it. If
the performer moves very quickly, the apparent motionof the android differs. Moreover, a lack of higher orderproperties prevents the system from adequately compensating for the dynamic characteristics of the android andthe delay of the feedforward network.
• The proposed method is limited by the speed of motion.
It is necessary to consider the properties to overcome therestriction, although the android has absolute physicallimitations such as a ﬁxed compliance and a maximum
speed that is less than that of a typical human being.
Although physical limitations cannot be overcome by any
control method, there are ways of ﬁnessing them to ensure
movements still look natural. For example, although theandroid lacks the opponent musculature of human beings,
which affords a variable compliance of the joints, the wobbly
appearance of such movements as rapid waving, which arehigh in both speed and frequency, can be overcome byslowing the movement and removing repeated closed curvesin the joint angle space to eliminate lag caused by the slowed
movement. If the goal is humanlike movement, one approach
may be to query a database of movements that are known tobe humanlike to ﬁnd the one most similar to the movement
made by the performer, although this begs the question ofwhere those movements came from in the ﬁrst place. Anothermethod is to establish criteria for evaluating the naturalnessof a movement [10]. This is an area for future study.
C. Required improvement and future work
In this paper we focus on reproducing positional changes
at the body’s surface rather than changes in the joint angles
to generate the android’s movement. Fig. 5(a) is a straightforward method to implement the idea. This paper has adopted
the transformation T from marker positions to estimated joint
angles because it is difﬁcult to derive a feedback controller
which produces the control input ∆q b only from the error in
the marker’s positional error ∆xd analytically. We actually
do not know which joints should be moved to remove a
positional error at the body’s surface. This relation mustbe learned, however, the transformation T could disturb thelearing. Hence, it is not generally guaranteed that the feedback
controller which converges the estimated joint angle q̂ a toq̂ h enables the marker’s position xa to approach xh . The
assumption that the android’s body surfaces are reasonably
close to the performer’s could avoid this problem, but the
feedback controller shown in Fig. 5(a) is essentially necessary
for mapping the apparent motion. It is possible to ﬁnd out
how the joint changes relate to the movements of bodysurfaces by analyzing the weights of the neural network of
the feedforward controller. A feedback controller could bedesigned to output the control input based on the error in themarker’s position with the analyzed relation. Concerning thedesign of the feedback controller, Oyama et al. [22], [23], [24]proposed several methods for learning both of feedback andfeedforward controllers using neural networks. This is one
potential method to obtain the feedback controller shown in
Fig. 5(a). Assessment of and compensation for deformationand displacement of the human skin, which cause marker

movement with respect to the underlying bone [25], are also
useful in designing the feedback controller.
We have not dealt with the android’s gaze and facial
expressions in the experiment; however, if gaze and facial
expressions are unrelated to hand gestures and body movements, the appearance is often unnatural, as we have found in
our experiments. Therefore, to make the android’s movement
appear more natural, we have to consider a method to implement the android’s eye movements and facial expressions.
V. C ONCLUSION
This paper has proposed a method of implementing humanlike motions by mapping their three-dimensional appearance
to the android using a motion capture system. By measuring
the android’s posture and comparing it to the posture of ahuman performer, we propose a new method to evaluate motion sequences along bodily surfaces. Unlike other approaches
that focus on reducing joint angle errors, we consider how toevaluate differences in the android’s apparent motion, that is,motion at its visible surfaces. The experimental results show
the effectiveness of the evaluation: the method can transfer
human motion. However, the method is restricted by the speedof the motion. We have to introduce a method to deal withthe dynamic characteristics and physical limitations of theandroid. We also have to evaluate the method with different
performers. We would expect to generate the most natural andaccurate movements using a female performer who is about
the same height as the original woman on which the android
is based. Moreover, we have to evaluate the human likeness ofthe visible motions by the subjective impressions the android
gives experimental subjects and the responses it elicits, suchas eye contact [26], [27], autonomic responses, and so on.
Research in these areas is in progress.
ACKNOWLEDGMENT
We developed the android in collaboration with Kokoro
Company, Ltd.
R EFERENCES[1] T. Kanda, H. Ishiguro, T. Ono, M. Imai, and K. Mase, “Development
and evaluation of an interactive robot “Robovie”,” in Proceedings ofthe IEEE International Conference on Robotics and Automation, 2002,
pp. 1848–1855.
[2] J. Goetz, S. Kiesler, and A. Powers, “Matching robot appearance andbehavior to tasks to improve human-robot cooperation,” in Proceedingsof the Workshop on Robot and Human Interactive Communication,
2003, pp. 55–60.
[3] C. F. DiSalvo, F. Gemperle, J. Forlizzi, and S. Kiesler, “All robots are
not created equal: The design and perception of humanoid robot heads,”
in Proceedings of the Symposium on Designing Interactive Systems,
2002, pp. 321–326.
[4] K. Grill-Spector, N. Knouf, and N. Kanwisher, “The fusiform face areasubserves face perception, not generic within-category identiﬁcation,”
Nature Neuroscience, vol. 7, no. 5, pp. 555–562, 2004.
[5] M. J. Farah, C. Rabinowitz, G. E. Quinn, and G. T. Liu, “Early
commitment of neural substrates for face recognition,” Cognitive Neuropsychology, vol. 17, pp. 117–123, 2000.
[6] D. Carmel and S. Bentin, “Domain speciﬁcity versus expertise: Factors
inﬂuencing distinct processing of faces,” Cognition, vol. 83, pp. 1–29,
2002.

[7] T. Minato, K. F. MacDorman, M. Shimada, S. Itakura, K. Lee,and H. Ishiguro, “Evaluating humanlikeness by comparing responses
elicited by an android and a person,” in Proceedings of the 2nd
International Workshop on Man-Machine Symbiotic Systems, 2004, pp.
373–383.
[8] M. Okada, S. Ban, and Y. Nakamura, “Skill of compliance with
controlled charging/discharging of kinetic energy,” in Proceeding of
the IEEE International Conference on Robotics and Automation, 2002,
pp. 2455–2460.
[9] T. Yoshikai, I. Mizuuchi, D. Sato, S. Yoshida, M. Inaba, and H. Inoue,“Behavior system design and implementation in spined musle-tendon
humanoid ”Kenta”,” Journal of Robotics and Mechatronics, vol. 15,
no. 2, pp. 143–152, 2003.
[10] T. Kashima and Y. Isurugi, “Trajectory formation based on physiological characteristics of skeletal muscles,” Biological Cybernetics, vol. 78,
no. 6, pp. 413–422, 1998.
[11] M. Riley, A. Ude, and C. G. Atkeson, “Methods for motion generationand interaction with a humanoid robot: Case studies of dancing andcatching,” in Proceedings of AAAI and CMU Workshop on Interactive
Robotics and Entertainment, 2000.
[12] S. Nakaoka, A. Nakazawa, K. Yokoi, H. Hirukawa, and K. Ikeuchi,“Generating whole body motions for a biped humanoid robot from
captured human dances,” in Proceedings of the 2003 IEEE International
Conference on Robotics and Automation, 2003.
[13] J. G. Hale, F. E. Pollick, and M. Tzoneva, “The visual categorizationof humanoid movement as natural,” in Proceedings of the Third IEEE
International Conference on Humanoid Robotics, 2003.
[14] M. Gleicher, “Retargetting motion to new characters,” in Proceedingsof the International Conference on Computer Graphics andInteractive
Techniques, 1998, pp. 33–42.
[15] M. Mori, “Bukimi no tani [the uncanny valley] (in Japanese),” Energy,
vol. 7, no. 4, pp. 33–35, 1970.
[16] T. Fong, I. Nourbakhsh, and K. Dautenhahn, “A survey of sociallyinteractive robots,” Robotics and Autonomous Systems, vol. 42, pp. 143–
166, 2003.
[17] T. Minato, M. Shimada, H. Ishiguro, and S. Itakura, “Development ofan android robot for studying human-robot interaction,” in Proceedingsof the 17th International Conference on Industrial & Engineering
Applications of Artiﬁcial Intelligence & Expert Systems, 2004, pp. 424–
434.
[18] M. Kawato, K. Furukawa, and R. Suzuki, “A hierarchical neuralnetwork model for control and learning of voluntary movement,”
Biological Cybernetics, vol. 57, pp. 169–185, 1987.
[19] E. S. Grood and W. J. Suntay, “A joint coordinate system for theclinical description of three-dimensional motions: Application to theknee,” Journal of Biomechanical Engineering, vol. 105, pp. 136–144,
1983.
[20] J. H. Challis, “A procedure for determining rigid body transformation
parameters,” Journal of Biomechanics, vol. 28, pp. 733–737, 1995.
[21] F. E. Veldpaus, H. J. Woltring, and L. J. M. G. Dortmans, “A leastsquares algorithm for the equiform transformation from spatial marker
co-ordinates,” Journal of Biomechanics, vol. 21, pp. 45–54, 1988.
[22] E. Oyama, N. Y. Chong, A. Agah, T. Maeda, S. Tachi, and K. F.
MacDorman, “Learning a coordinate transformation for a human visualfeedback controller based on disturbance noise and the feedback
error signal,” in Proceedings of the IEEE International Conference on
Robotics and Automation, 2001.
[23] E. Oyama, K. F. MacDorman, A. Agah, T. Maeda, and S. Tachi, “Coordinate transformation learning of a hand position feedback controller
with time delay,” Neurocomputing, vol. 38–40, no. 1–4, 2001.
[24] E. Oyama, A. Agah, K. F. MacDorman, T. Maeda, and S. Tachi,“A modular neural network architecture for inverse kinematics model
learning,” Neurocomputing, vol. 38–40, no. 1–4, pp. 797–805, 2001.
[25] A. Leardini, L. Chiari, U. D. Croce, and A. Cappozzo, “Humanmovement analysis using stereophotogrammetry Part 3. Soft tissue
artifact assessment and compensation,” Gait and Posture, vol. 21, pp.
212–225, 2005.
[26] T. Minato, M. Shimada, S. Itakura, K. Lee, and H. Ishiguro, “Does gazereveal the human likeness of an android?” in Proceedings of the 4thIEEE International Conference on Development and Learning, 2005.
[27] K. F. MacDorman, T. Minato, M. Shimada, S. Itakura, S. Cowley, and
H. Ishiguro, “Assessing human likeness by eye contact in an android
testbed,” in Proceedings of the XXVII Annual Meeting of the Cognitive
Science Society, 2005.